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Many datasets are intrinsically hierarchical: geographic entities, such as census blocks, census tracts, counties and states; the command structure of businesses and governments; file systems; software packages. And even non-hierarchical data may be arranged hierarchically as with k-means clustering or phylogenetic trees. A good hierarchical visualization facilitates rapid multiscale inference: micro-observations of individual elements and macro-observations of large groups.

This module implements several popular techniques for visualizing hierarchical data:

Node-link diagrams show topology using discrete marks for nodes and links, such as a circle for each node and a line connecting each parent and child. The “tidy” tree is delightfully compact, while the dendrogram places leaves at the same level. (These have both polar and Cartesian forms.) Indented trees are useful for interactive browsing.

Adjacency diagrams show topology through the relative placement of nodes. They may also encode a quantitative dimension in the area of each node, for example to show revenue or file size. The “icicle” diagram uses rectangles, while the “sunburst” uses annular segments.

Enclosure diagrams also use an area encoding, but show topology through containment. A treemap recursively subdivides area into rectangles. Circle-packing tightly nests circles; this is not as space-efficient as a treemap, but perhaps more readily shows topology.

See one of:

  • Hierarchies - represent and manipulate hierarchical data
  • Stratify - organize tabular data into a hierarchy
  • Tree - construct “tidy” tree diagrams of hierarchies
  • Cluster - construct tree diagrams that place leaf nodes at the same depth
  • Partition - construct space-filling adjacency diagrams
  • Pack - construct enclosure diagrams by tightly nesting circles
  • Treemap - recursively subdivide rectangles by quantitative value